bivariate.mixalg: EM algorithm and classification for univariate data, for... In CAMAN: Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN

Function

Usage

 1 2 3 bivariate.mixalg(obs1, obs2, type, data = NULL, var1, var2, corr, lambda1, lambda2, p,startk, numiter=5000, acc=1.e-7, class)

Arguments

 obs1 the first column of the observations

 obs2 the second column of the observations

 type kind of data

 data an optional data frame

 var1 Variance of the first column of the observations(except meta-analysis)

 var2 Variance of the second column of the observations (except meta-analysis)

 corr correlation coefficient

 lambda1 Means of the first column of the observations

 lambda2 Means of the second column of the observations

 p Probability

 startk starting/maximal number of components. This number will be used to compute the grid in the VEM. Default is 20.

 numiter parameter to control the maximal number of iterations in the VEM and EM loops. Default is 5000.

 acc convergence criterion. Default is 1.e-7

 class classification of studies

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ## Not run: #1.EM and classification for bivariate data #Examples data(rs12363681) test <- bivariate.mixalg(obs1=x, obs2=y, type="bi", lambda1=0, lambda2=0, p=0, data=rs12363681, startk=20, class="TRUE") #scatter plot with ellipse plot(test) #scatter plot without ellipse plot(test, ellipse = FALSE) #2.EM and classification for meta data #Examples data(CT) bivariate.mixalg(obs1=logitTPR, obs2=logitTNR, var1=varlogitTPR, var2=varlogitTNR, type="meta", lambda1=0, lambda2=0, p=0,data=CT,startk=20,class="TRUE") ## End(Not run)

CAMAN documentation built on May 1, 2019, 9:21 p.m.